Various types of data are acquired in real time by sensors with the progress of IoT (Internet of Things) in recent years. Such real-time data acquisition makes it possible to grasp a state of a monitoring target in real time.
Attempts have also been made to detect an abnormality of a monitoring target on the basis of chronological data (time series sensor data) acquired by sensors. However, in normal cases, most of the pieces of the time series sensor data are records of normality and rarely include records of abnormality. Also, since the number of sensors is enormous, the data volume of the time series sensor data also becomes enormous. In addition, since there are various patterns of data indicating abnormality (abnormal patterns), undiscovered, abnormal patterns may be included in the time series sensor data. Accordingly, it is not a good idea to manually create feature values of the individual abnormal patterns for detection of abnormality.
In view of the above, a scheme is used according to which, learning patterns (normal patterns) indicating the normality of the monitoring target are learned by using methodology such as a regression model, deep learning, etc. on the basis of learning state data indicative of a normal state of a monitoring target, and then an abnormality is detected on the basis of the degree of deviation of the time series sensor data relative to the normal patterns. According to this scheme, it is only necessary to acquire time series sensor data in normal states, and prior knowledge about abnormal patterns, sensors, etc. is not required. As a result, the scheme is expected to foe applied to various fields.
Also, in order to detect abnormality in real time, prediction of future normal patterns in advance is carried out. For example, a normal pattern at a predetermined time in the future is predicted on the basis of time series sensor data up to the present time. When the predetermined time is reached, the time series sensor data at the predetermined time is compared with the predicted normal pattern and a prediction error, which is the difference between them, is calculated. Such prediction is readily made when the monitoring target is in a normal state, so that the prediction error is expected to be small. Since the prediction is difficult when the monitoring target is abnormal, the prediction error is expected to be large. In this manner, the current prediction error can be calculated on the basis of the previously predicted normal patterns, and the current abnormality can be detected. Accordingly, abnormality can be detected in real time.
The accuracy of the abnormality detection based on the prediction error largely depends upon the prediction accuracy of an abnormality detection apparatus. However, it is difficult to improve the prediction accuracy with existing technology that makes prediction using a single deterministic model. For example, noise is often included even if sensor data that has been actually acquired is functioning normally, and the prediction accuracy deteriorates due to the noise. Also, even when the monitoring target behaves periodically, the cycle is not necessarily constant, so that the prediction may be temporally delayed. As a result, there may be cases where the prediction error is small even if the monitoring target is in an abnormal state, or the prediction error is large even if the monitoring target is in a normal state. Hence, it is difficult to accurately detect abnormalities relying upon the existing technology.